Type 1 and Type 2 error.
Type I error is the rejection of a true null hypothesis (also known as a “false positive” finding or conclusion)
Type II error is the non-rejection of a false null hypothesis (also known as a “false negative” finding or conclusion)
In statistical hypothesis testing “type I” and “type II” errors are, respectively, the incorrect rejection of a true null hypothesis and the failure to reject a false null hypothesis.
In other words:
- A type I error is detecting an effect that is not present.
- A type II error is failing to detect an effect that is present.